摘要
为了缩小图像高层语义与底层特征之间的鸿沟,提出了一种基于共生矩阵的图像纹理特征提取的新方法.该方法结合了图像的频域统计特征和空间分布特性,首先通过小波变换提取图像的局部频域信息,然后结合图像的整体结构特征,构建用于提取图像纹理特征的小波灰度共生矩阵.通过对比实验表明,与分别使用其他灰度共生矩阵和小波特征相比,基于小波灰度共生矩阵的纹理特征提取方法在医学图像检索中取得了更好的效果.
To eliminate the gap between the high-level semantics and the low-level features of images, a new method based on co-occurrence matrix is proposed to extract image texture feature. Combining the image＇s statistical features in the frequency domain with its spatial distribution attributes, the method extracts the local frequency information on image by wavelet transforming. Then the image＇s global structural characteristics are integrated with its wavelet properties to construct the wavelet-gray co-occurrence matrix so as to extract the image＇s texture features for the retrieval of medical images. The results of comparative test showed that the wavelet-gray co-occurrence matrix is superior in medical image retrieval in comparison to other ways where the co-occurrence matrix is separated from wavelet features in applications.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第3期341-344,共4页
Journal of Northeastern University(Natural Science)
基金
国家重点基础研究发展规划项目(2006CB303103)
关键词
共生矩阵
纹理
小波变换
特征提取
图像检索
co-occurrence matrix
texture
wavelet transforming
feature extraction
image retrieval
